Issue No. 01 - Jan. (2014 vol. 25)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPDS.2013.41
Xiaodong Liu , Sch. of Comput., Nat. Univ. of Defense Technol., Changsha, China
Mo Li , Sch. of Comput. Eng., Nanyang Technol. Univ., Singapore, Singapore
Shanshan Li , Sch. of Comput., Nat. Univ. of Defense Technol., Changsha, China
Shaoliang Peng , Sch. of Comput., Nat. Univ. of Defense Technol., Changsha, China
Xiangke Liao , Sch. of Comput., Nat. Univ. of Defense Technol., Changsha, China
Xiaopei Lu , Sch. of Comput., Nat. Univ. of Defense Technol., Changsha, China
Influence Maximization aims to find the top-$(K)$ influential individuals to maximize the influence spread within a social network, which remains an important yet challenging problem. Proven to be NP-hard, the influence maximization problem attracts tremendous studies. Though there exist basic greedy algorithms which may provide good approximation to optimal result, they mainly suffer from low computational efficiency and excessively long execution time, limiting the application to large-scale social networks. In this paper, we present IMGPU, a novel framework to accelerate the influence maximization by leveraging the parallel processing capability of graphics processing unit (GPU). We first improve the existing greedy algorithms and design a bottom-up traversal algorithm with GPU implementation, which contains inherent parallelism. To best fit the proposed influence maximization algorithm with the GPU architecture, we further develop an adaptive K-level combination method to maximize the parallelism and reorganize the influence graph to minimize the potential divergence. We carry out comprehensive experiments with both real-world and sythetic social network traces and demonstrate that with IMGPU framework, we are able to outperform the state-of-the-art influence maximization algorithm up to a factor of 60, and show potential to scale up to extraordinarily large-scale networks.
Graphics processing units, Social network services, Parallel processing, Acceleration, Instruction sets, Computational modeling, Accuracy
Xiaodong Liu, Mo Li, Shanshan Li, Shaoliang Peng, Xiangke Liao and Xiaopei Lu, "IMGPU: GPU-Accelerated Influence Maximization in Large-Scale Social Networks," in IEEE Transactions on Parallel & Distributed Systems, vol. 25, no. 1, pp. 136-145, 2013.